I wish more business / product people understood this concept. When a product has been refined enough to approach Pareto optimality (at least on the dimensions the product is easily measured), it's all too common for people to chase improvements to one metric at a time, and when that runs out, switch to another metric. This results in going in circles (make metric A go up-up-up, forcing metric B down-down-down, then make B go up-up-up while forcing A to go down-down-down - it's worse than this because multiple dimensions go up/down together, making it harder to spot). Sometimes these cycles are over a period of quarters or years, making it even harder to spot because cycles are slower than employee attrition.

This is not independent of Goodhart's Law[1]. I've seen entire product orgs, on a very mature product (i.e., nearing the Pareto frontier for the metrics that are tracked), assign one metric per PM and tie PM comp to their individual metric improving. Then PMs wheel and deal away good features because "don't ship your thing that hurts my metric and I won't ship my thing that hurts yours" - and that's completely rational given the incentives. Of course the best wheelers-and-dealers get the money/promotions. So the games escalate ("you didn't deal last time, so it's going to cost you more this time"). Eventually negative politics explode and it's all just a reality TV show. Meanwhile engineers who don't have an inside view of what's going on are left wondering why PMs appear to be acting insane with ship/no-ship decisions.

If more people understood Pareto optimality and Goodhart's Law, even at a surface level, I think being "data driven" would be a much better thing.

[1] Goodhart's Law: when a measure becomes a target, it ceases to be a good measure